import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import tensorflow as tf

class NN(object):
    def __init__(self):
        self.df = pd.read_csv('NN/scenic_data.csv')



    def create_dataset(self, data, n_steps):
        """构造数据
        """
        x, y = [], []
        for i in range(len(data) - n_steps):
            x.append(data[i:i+n_steps])
            y.append(data[i+n_steps, :18])
        return np.array(X), np.array(y)

    def get_model(self):
        n_steps = 7  # 长度7天
        data = self.df.values
        X, y = self.create_dataset(data, n_steps)

    #
    train_size = int(len(X) * 0.8)
    X_train,X_test = X[:train_size],X[:train_size]
    y_train,y_test = y[:train_size],y[:train_size]

    # 假设 X_train, y_train, X_test, y_test 已经定义
    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.LSTM(50, activation='relu', return_sequences=True, input_shape=(n_steps, X_train.shape[2])))
    model.add(tf.keras.layers.LSTM(50, activation='relu'))
    model.add(tf.keras.layers.Dense(18))
    model.compile(optimizer='adam', loss='mse')
    model.fit(X_train, y_train, epochs=50, validation_data=(X_test, y_test))

    # 评估
    loss = model.evaluate(X_test, y_test)
    print(f"测试损失: {loss:.}")

    # 保存模型
    tf.keras.models.save_model(model, 'NN/my_model.keras')

if __name__ == '__main__':
    nn = NN()
    nn.get_model()
